The cells of the immune system coordinate the body’s response to infection and injury. An understanding of these cells at the molecular level can facilitate the elucidation of underlying mechanisms and improve patient treatment. We aim to characterize the immune response to trauma at the protein level to determine if differences in the immune response correlate with severity of inflammatory response and can explain differing outcomes in trauma patients.
Using mass spectrometry-based proteomics, we have characterized the proteomes of T-cells, monocytes, and neutrophils in over 2,000 clinical samples taken from over 200 patients at seven time points following traumatic injury. Bioinformatic analysis will now be applied to identify proteins that are correlated with patients’ clinical outcomes.
This work is currently the largest clinical proteomic study of the human immune system and will enable us to generate a database of T-cells, monocytes, and neutrophil proteins. Our longitudinal approach will enable for the first time a study of the temporal response to trauma by the immune system at the protein level. By integrating this data with existing gene data, we will be able to provide a large-scale assessment of the relationship between genomics and proteomics in a clinical population.
Immunoproteomics database. We detected 4,897 proteins from pooled samples of T-cells, monocytes, and neutrophils, from each of patient, control, and ex-vivo conditions using LC-MS/MS. This cell-specific immunoproteomics database will lay a foundation for a comprehensive analysis of the trauma-associated alterations of immunoproteome.
Longitudinal Clinical Proteomics. Samples of T-cells and monoyctes from over 200 patients with severe injury were collected at 12 hours, 1, 4, 7, 14, 21, and 28 days after injury and analyzed using 18O labeling approach. We aim to study proteins associated with clinical outcome in these trauma patients.
We have collected and completed mass spectrometry analyses of over 2,000 samples from over 200 patients. Our preliminary results identify 304 proteins are differentially expressed between patients and controls (q-value < 0.05).
We have completed preliminary studies on correlation between proteins and genes from clinical samples.
Analysis of temporal trajectories of trauma patient proteins. We aim to identify proteins that predict recovery time of patients from early samples. Integration of proteomics and clinical phenotype. Combination of clinical and molecular data should provide more predictive power than either alone. Comparison of proteomics and genomics at the individual level. We aim to determine the extent to which gene expression can be correlated to protein levels in healthy and trauma populations.
Soyoung Ryu1, Amit Kaushal1, Hong Gao1, Weijun Qian2, Weihong Xu1, David Camp2, Ron Davis1, Richard Smith2, Ron Tompkins3, and Wenzhong Xiao1,3
1 Stanford Genome Technology Center, 2 Pacific Northwest National Laboratory, 3. Massachusetts General Hospital